With the rise of AI across every industry, the buzzwords are flying fast—AI infrastructure, infrastructure for AI workloads, autonomous infrastructure, and more. The problem? These terms are often used interchangeably, and it’s easy to get lost in the noise.
But understanding the foundation of how AI runs—and what supports it—is critical to scaling your efforts effectively. Whether you’re building models or running inference at scale, the infrastructure choices you make will directly impact performance, cost, and speed to market.
As you navigate the landscape, keep these 9 essential definitions in mind to cut through the confusion and build smarter, faster AI systems.
The compute, storage, networking, and software layers that support the development, training, deployment, and inference of artificial intelligence models.
AI infrastructure includes GPUs/TPUs, high-throughput storage, scalable compute and container orchestration, like Kubernetes. It must be performant, scalable, and often cloud native to meet the demands of modern AI workloads. It’s the foundation AI teams rely on—built to be reliable, flexible, and efficient.
Infrastructure that is run by or automated with AI to improve operations, scaling, and decision-making.
Think self-healing systems, predictive scaling, anomaly detection, and AI-powered cost optimization. AI here isn’t the workload—it’s what’s making the infrastructure smarter, more autonomous, and easier to manage.
A visionary definition, infrastructure that uses AI to manage and optimize itself with minimal human intervention.
This includes self-healing clusters, predictive autoscaling, anomaly detection, and performance optimization driven by machine learning. The goal is to reduce toil and increase reliability as systems grow in complexity.
The application of machine learning and data analytics to automate and enhance IT operations.
AI Ops systems analyze logs, metrics, and traces to detect issues, predict outages, and recommend or implement fixes. It’s a key enabler of autonomous infrastructure.
The process of efficiently scheduling AI workloads on available GPUs across a multi-tenant environment to maximize performance and resource utilization.
Efficient GPU scheduling is essential for cost control and performance, especially in Kubernetes clusters running multiple AI workloads.
The system responsible for deploying trained models in production so they can make real-time or batch predictions.
This includes tools like KServe, TorchServe, and NVIDIA Triton, and involves version control, autoscaling, load balancing, and monitoring.
A centralized repository for storing, managing, and sharing features used in machine learning models.
Feature stores are essential for consistency between training and inference, and for operationalizing ML pipelines at scale.
The infrastructure optimized for serving predictions from AI models—often in real time—with low latency and high throughput.
This can include edge compute, autoscaled inference clusters, and model optimization for deployment (e.g., quantization, pruning).
The discipline of managing the lifecycle of machine learning models—from development to deployment to monitoring and retraining.
MLOps combines CI/CD principles with model versioning, governance, monitoring, and performance tracking to ensure models remain accurate and reliable over time.
Organizations investing in AI need cloud native infrastructure that can scale with their workloads, manage GPUs efficiently, support complex pipelines, and offer strong observability. But most teams don’t have time to manage Kubernetes upgrades, patch security vulnerabilities, or build optimized models serving stacks from scratch.
That’s why it makes sense to focus on what differentiates you—your models and your data—and let a Managed Kubernetes-as-a-Service provider handle the complexity of the infrastructure underneath.
Learn more about Fairwinds AI-ready infrastructure.
